Evaluation of rice germplasm for genetic diversity on yield characters by principal component analysis
Author(s):
Sudhir K Pathak, G Roopa Lavanya, G Suresh Babu and Neha Srivastava
Abstract:
A population comprising 98 rice genotypes were evaluated for 13 agro - morphological traits by principal component analysis for determining the pattern of genetic diver¬sity and relationship among individuals. Thirteen quantitative characters i.e. plant height, leaf length, leaf width, number of productive tillers per plant, panicle per plant, panicle length, number of grains per panicle, days to 50% flowering, days to harvest maturity, biological yield, harvest index, test weight and single plant yield were measured. Maximum variation was observed for flag leaf width followed by number of panicles per plant, tillers per plant, test weight and flag leaf length. Days to maturity has shown the least variation. In PCA, Component 1 had the contribution from the traits such as days to maturity and harvest index which accounted 25.34 % of the total variability. Plant height, biological yield per plant and grain yield per plant has contributed 19.30% of total variability in component 2. The remaining variability of 11.86%, 10.26%, 7.61% and 5.64% was consolidated in component 3, component 4, component 5 and component 6 by various traits such as flag leaf width, number of productive tillers per plant, days to 50% flowering, spikelets per plant, flag leaf length, panicles per plant, test weight and panicle length. The cumulative variance of 80.04% of total variation among 13 characters was explained by the first six axes. Thus the results of principal component analysis used in the study have revealed the high level of genetic variation and the traits contributing for the variation was identified. Hence this population can be utilized for trait improvement in breeding programs for the traits contributing major variation.
How to cite this article:
Sudhir K Pathak, G Roopa Lavanya, G Suresh Babu, Neha Srivastava. Evaluation of rice germplasm for genetic diversity on yield characters by principal component analysis. Pharma Innovation 2018;7(4):661-664.